Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
HESS | Articles | Volume 23, issue 10
Hydrol. Earth Syst. Sci., 23, 4323–4331, 2019
https://doi.org/10.5194/hess-23-4323-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 4323–4331, 2019
https://doi.org/10.5194/hess-23-4323-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 25 Oct 2019

Technical note | 25 Oct 2019

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

Wouter J. M. Knoben et al.

Related authors

Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations
Wouter J. M. Knoben, Jim E. Freer, Keirnan J. A. Fowler, Murray C. Peel, and Ross A. Woods
Geosci. Model Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019,https://doi.org/10.5194/gmd-12-2463-2019, 2019
Short summary
DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology
Gemma Coxon, Jim Freer, Rosanna Lane, Toby Dunne, Wouter J. M. Knoben, Nicholas J. K. Howden, Niall Quinn, Thorsten Wagener, and Ross Woods
Geosci. Model Dev., 12, 2285–2306, https://doi.org/10.5194/gmd-12-2285-2019,https://doi.org/10.5194/gmd-12-2285-2019, 2019
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
On the configuration and initialization of a large-scale hydrological land surface model to represent permafrost
Mohamed E. Elshamy, Daniel Princz, Gonzalo Sapriza-Azuri, Mohamed S. Abdelhamed, Al Pietroniro, Howard S. Wheater, and Saman Razavi
Hydrol. Earth Syst. Sci., 24, 349–379, https://doi.org/10.5194/hess-24-349-2020,https://doi.org/10.5194/hess-24-349-2020, 2020
Short summary
On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments
Thanh Duc Dang, A. F. M. Kamal Chowdhury, and Stefano Galelli
Hydrol. Earth Syst. Sci., 24, 397–416, https://doi.org/10.5194/hess-24-397-2020,https://doi.org/10.5194/hess-24-397-2020, 2020
Short summary
A global Budyko model to partition evaporation into interception and transpiration
Ameneh Mianabadi, Miriam Coenders-Gerrits, Pooya Shirazi, Bijan Ghahraman, and Amin Alizadeh
Hydrol. Earth Syst. Sci., 23, 4983–5000, https://doi.org/10.5194/hess-23-4983-2019,https://doi.org/10.5194/hess-23-4983-2019, 2019
Short summary
Are the effects of vegetation and soil changes as important as climate change impacts on hydrological processes?
Kabir Rasouli, John W. Pomeroy, and Paul H. Whitfield
Hydrol. Earth Syst. Sci., 23, 4933–4954, https://doi.org/10.5194/hess-23-4933-2019,https://doi.org/10.5194/hess-23-4933-2019, 2019
Short summary
Expansion and contraction of the flowing stream network alter hillslope flowpath lengths and the shape of the travel time distribution
H. J. Ilja van Meerveld, James W. Kirchner, Marc J. P. Vis, Rick S. Assendelft, and Jan Seibert
Hydrol. Earth Syst. Sci., 23, 4825–4834, https://doi.org/10.5194/hess-23-4825-2019,https://doi.org/10.5194/hess-23-4825-2019, 2019
Short summary

Cited articles

Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827, https://doi.org/10.5194/gmd-5-819-2012, 2012. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017a. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies. version 2.0., UCAR/NCAR, Boulder, CO, USA, https://doi.org/10.5065/D6G73C3Q, 2017b. 
Andersson, J. C. M., Arheimer, B., Traoré, F., Gustafsson, D., and Ali, A.: Process refinements improve a hydrological model concept applied to the Niger River basin, Hydrol. Process., 31, 4540–4554, https://doi.org/10.1002/hyp.11376, 2017. 
Beven, K. J., Younger, P. M., and Freer, J.: Struggling with Epistemic Uncertainties in Environmental Modelling of Natural Hazards, in: Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA), 13–16 July 2014, Liverpool, UK, American Society of Civil Engineers, 13–22, 2014. 
Publications Copernicus
Download
Short summary
The accuracy of model simulations can be quantified with so-called efficiency metrics. The Nash–Sutcliffe efficiency (NSE) has been often used in hydrology, but recently the Kling–Gupta efficiency (KGE) is gaining in popularity. We show that lessons learned about which NSE scores are acceptable do not necessarily translate well into understanding of the KGE metric.
The accuracy of model simulations can be quantified with so-called efficiency metrics. The...
Citation